python - How to select the last column of dataframe

Python - How to select the last column of dataframe

You can select the last column of a Pandas DataFrame using the column's label or by using integer indexing. Here are two ways to achieve this:

  1. Using Column Label:
import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Select the last column by label last_column_label = df.columns[-1] last_column = df[last_column_label] print("Last Column by Label:") print(last_column)
  1. Using Integer Indexing:
import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Select the last column by integer indexing last_column = df.iloc[:, -1] print("Last Column by Integer Indexing:") print(last_column)

Both approaches will give you the last column of the DataFrame. Choose the one that fits your needs or coding style.

Examples

  1. Select the Last Column of a Pandas DataFrame Using Negative Index:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using negative index last_column = df.iloc[:, -1]

    Description: Use negative index -1 with iloc to select the last column of a Pandas DataFrame.

  2. Select the Last Column of a DataFrame Using iloc and shape:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc and shape last_column = df.iloc[:, df.shape[1] - 1]

    Description: Use iloc along with shape to dynamically select the last column of a Pandas DataFrame.

  3. Select the Last Column of a DataFrame Using iloc and iloc with max:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc with max last_column = df.iloc[:, max(df.columns)]

    Description: Use iloc with max on the columns to select the last column of a Pandas DataFrame.

  4. Select the Last Column of a DataFrame Using iloc with columns Property:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc and columns property last_column = df.iloc[:, df.columns[-1]]

    Description: Use iloc with the columns property to select the last column of a Pandas DataFrame.

  5. Select the Last Column of a DataFrame Using iloc and loc:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc and loc last_column = df.loc[:, df.columns[-1]]

    Description: Combine iloc and loc to select the last column of a Pandas DataFrame.

  6. Select the Last Column of a DataFrame Using iloc with Slicing:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc with slicing last_column = df.iloc[:, -1:]

    Description: Use iloc with slicing to select the last column of a Pandas DataFrame.

  7. Select the Last Column of a DataFrame Using iloc and ix:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc and ix last_column = df.ix[:, -1]

    Description: Use iloc and ix to select the last column of a Pandas DataFrame. Note: ix is deprecated in newer versions of Pandas.

  8. Select the Last Column of a DataFrame Using iloc with tail:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc with tail last_column = df.iloc[:, -1:].tail(1)

    Description: Use iloc with tail to select the last column of a Pandas DataFrame and return it as a DataFrame.

  9. Select the Last Column of a DataFrame Using iloc and at:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc and at last_column = df.at[:, df.columns[-1]]

    Description: Use iloc and at to select the last column of a Pandas DataFrame.

  10. Select the Last Column of a DataFrame Using iloc and iat:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the last column using iloc and iat last_column = df.iat[:, -1]

    Description: Use iloc and iat to select the last column of a Pandas DataFrame. Note: iat is faster than at but works only with integer indices.

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